{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detrending, Stylized Facts and the Business Cycle\n",
    "\n",
    "In an influential article, Harvey and Jaeger (1993) described the use of unobserved components models (also known as \"structural time series models\") to derive stylized facts of the business cycle.\n",
    "\n",
    "Their paper begins:\n",
    "\n",
    "    \"Establishing the 'stylized facts' associated with a set of time series is widely considered a crucial step\n",
    "    in macroeconomic research ... For such facts to be useful they should (1) be consistent with the stochastic\n",
    "    properties of the data and (2) present meaningful information.\"\n",
    "    \n",
    "In particular, they make the argument that these goals are often better met using the unobserved components approach rather than the popular Hodrick-Prescott filter or Box-Jenkins ARIMA modeling techniques.\n",
    "\n",
    "statsmodels has the ability to perform all three types of analysis, and below we follow the steps of their paper, using a slightly updated dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import display, Latex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unobserved Components\n",
    "\n",
    "The unobserved components model available in statsmodels can be written as:\n",
    "\n",
    "$$\n",
    "y_t = \\underbrace{\\mu_{t}}_{\\text{trend}} + \\underbrace{\\gamma_{t}}_{\\text{seasonal}} + \\underbrace{c_{t}}_{\\text{cycle}} + \\sum_{j=1}^k \\underbrace{\\beta_j x_{jt}}_{\\text{explanatory}} + \\underbrace{\\varepsilon_t}_{\\text{irregular}}\n",
    "$$\n",
    "\n",
    "see Durbin and Koopman 2012, Chapter 3 for notation and additional details. Notice that different specifications for the different individual components can support a wide range of models. The specific models considered in the paper and below are specializations of this general equation.\n",
    "\n",
    "### Trend\n",
    "\n",
    "The trend component is a dynamic extension of a regression model that includes an intercept and linear time-trend.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\underbrace{\\mu_{t+1}}_{\\text{level}} & = \\mu_t + \\nu_t + \\eta_{t+1} \\qquad & \\eta_{t+1} \\sim N(0, \\sigma_\\eta^2) \\\\\\\\\n",
    "\\underbrace{\\nu_{t+1}}_{\\text{trend}} & = \\nu_t + \\zeta_{t+1} & \\zeta_{t+1} \\sim N(0, \\sigma_\\zeta^2) \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where the level is a generalization of the intercept term that can dynamically vary across time, and the trend is a generalization of the time-trend such that the slope can dynamically vary across time.\n",
    "\n",
    "For both elements (level and trend), we can consider models in which:\n",
    "\n",
    "- The element is included vs excluded (if the trend is included, there must also be a level included).\n",
    "- The element is deterministic vs stochastic (i.e. whether or not the variance on the error term is confined to be zero or not)\n",
    "\n",
    "The only additional parameters to be estimated via MLE are the variances of any included stochastic components.\n",
    "\n",
    "This leads to the following specifications:\n",
    "\n",
    "|                                                                      | Level | Trend | Stochastic Level | Stochastic Trend |\n",
    "|----------------------------------------------------------------------|-------|-------|------------------|------------------|\n",
    "| Constant                                                             | ✓     |       |                  |                  |\n",
    "| Local Level <br /> (random walk)                                     | ✓     |       | ✓                |                  |\n",
    "| Deterministic trend                                                  | ✓     | ✓     |                  |                  |\n",
    "| Local level with deterministic trend <br /> (random walk with drift) | ✓     | ✓     | ✓                |                  |\n",
    "| Local linear trend                                                   | ✓     | ✓     | ✓                | ✓                |\n",
    "| Smooth trend <br /> (integrated random walk)                         | ✓     | ✓     |                  | ✓                |\n",
    "\n",
    "### Seasonal\n",
    "\n",
    "The seasonal component is written as:\n",
    "\n",
    "<span>$$\n",
    "\\gamma_t = - \\sum_{j=1}^{s-1} \\gamma_{t+1-j} + \\omega_t \\qquad \\omega_t \\sim N(0, \\sigma_\\omega^2)\n",
    "$$</span>\n",
    "\n",
    "The periodicity (number of seasons) is `s`, and the defining character is that (without the error term), the seasonal components sum to zero across one complete cycle. The inclusion of an error term allows the seasonal effects to vary over time.\n",
    "\n",
    "The variants of this model are:\n",
    "\n",
    "- The periodicity `s`\n",
    "- Whether or not to make the seasonal effects stochastic.\n",
    "\n",
    "If the seasonal effect is stochastic, then there is one additional parameter to estimate via MLE (the variance of the error term).\n",
    "\n",
    "### Cycle\n",
    "\n",
    "The cyclical component is intended to capture cyclical effects at time frames much longer than captured by the seasonal component. For example, in economics the cyclical term is often intended to capture the business cycle, and is then expected to have a period between \"1.5 and 12 years\" (see Durbin and Koopman).\n",
    "\n",
    "The cycle is written as:\n",
    "\n",
    "<span>$$\n",
    "\\begin{align}\n",
    "c_{t+1} & = c_t \\cos \\lambda_c + c_t^* \\sin \\lambda_c + \\tilde \\omega_t \\qquad & \\tilde \\omega_t \\sim N(0, \\sigma_{\\tilde \\omega}^2) \\\\\\\\\n",
    "c_{t+1}^* & = -c_t \\sin \\lambda_c + c_t^* \\cos \\lambda_c + \\tilde \\omega_t^* & \\tilde \\omega_t^* \\sim N(0, \\sigma_{\\tilde \\omega}^2)\n",
    "\\end{align}\n",
    "$$</span>\n",
    "\n",
    "The parameter $\\lambda_c$ (the frequency of the cycle) is an additional parameter to be estimated by MLE. If the seasonal effect is stochastic, then there is one another parameter to estimate (the variance of the error term - note that both of the error terms here share the same variance, but are assumed to have independent draws).\n",
    "\n",
    "### Irregular\n",
    "\n",
    "The irregular component is assumed to be a white noise error term. Its variance is a parameter to be estimated by MLE; i.e.\n",
    "\n",
    "$$\n",
    "\\varepsilon_t \\sim N(0, \\sigma_\\varepsilon^2)\n",
    "$$\n",
    "\n",
    "In some cases, we may want to generalize the irregular component to allow for autoregressive effects:\n",
    "\n",
    "$$\n",
    "\\varepsilon_t = \\rho(L) \\varepsilon_{t-1} + \\epsilon_t, \\qquad \\epsilon_t \\sim N(0, \\sigma_\\epsilon^2)\n",
    "$$\n",
    "\n",
    "In this case, the autoregressive parameters would also be estimated via MLE.\n",
    "\n",
    "### Regression effects\n",
    "\n",
    "We may want to allow for explanatory variables by including additional terms\n",
    "\n",
    "<span>$$\n",
    "\\sum_{j=1}^k \\beta_j x_{jt}\n",
    "$$</span>\n",
    "\n",
    "or for intervention effects by including\n",
    "\n",
    "<span>$$\n",
    "\\begin{align}\n",
    "\\delta w_t \\qquad \\text{where} \\qquad w_t & = 0, \\qquad t < \\tau, \\\\\\\\\n",
    "& = 1, \\qquad t \\ge \\tau\n",
    "\\end{align}\n",
    "$$</span>\n",
    "\n",
    "These additional parameters could be estimated via MLE or by including them as components of the state space formulation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Following Harvey and Jaeger, we will consider the following time series:\n",
    "\n",
    "- US real GNP, \"output\", ([GNPC96](https://research.stlouisfed.org/fred2/series/GNPC96))\n",
    "- US GNP implicit price deflator, \"prices\", ([GNPDEF](https://research.stlouisfed.org/fred2/series/GNPDEF))\n",
    "- US monetary base, \"money\", ([AMBSL](https://research.stlouisfed.org/fred2/series/AMBSL))\n",
    "\n",
    "The time frame in the original paper varied across series, but was broadly 1954-1989. Below we use data from the period 1948-2008 for all series. Although the unobserved components approach allows isolating a seasonal component within the model, the series considered in the paper, and here, are already seasonally adjusted.\n",
    "\n",
    "All data series considered here are taken from [Federal Reserve Economic Data (FRED)](https://research.stlouisfed.org/fred2/). Conveniently, the Python library [Pandas](https://pandas.pydata.org/) has the ability to download data from FRED directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Datasets\n",
    "from pandas_datareader.data import DataReader\n",
    "\n",
    "# Get the raw data\n",
    "start = '1948-01'\n",
    "end = '2008-01'\n",
    "us_gnp = DataReader('GNPC96', 'fred', start=start, end=end)\n",
    "us_gnp_deflator = DataReader('GNPDEF', 'fred', start=start, end=end)\n",
    "us_monetary_base = DataReader('AMBSL', 'fred', start=start, end=end).resample('QS').mean()\n",
    "recessions = DataReader('USRECQ', 'fred', start=start, end=end).resample('QS').last().values[:,0]\n",
    "\n",
    "# Construct the dataframe\n",
    "dta = pd.concat(map(np.log, (us_gnp, us_gnp_deflator, us_monetary_base)), axis=1)\n",
    "dta.columns = ['US GNP','US Prices','US monetary base']\n",
    "dates = dta.index._mpl_repr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a sense of these three variables over the timeframe, we can plot them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 936x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "ax = dta.plot(figsize=(13,3))\n",
    "ylim = ax.get_ylim()\n",
    "ax.xaxis.grid()\n",
    "ax.fill_between(dates, ylim[0]+1e-5, ylim[1]-1e-5, recessions, facecolor='k', alpha=0.1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model\n",
    "\n",
    "Since the data is already seasonally adjusted and there are no obvious explanatory variables, the generic model considered is:\n",
    "\n",
    "$$\n",
    "y_t = \\underbrace{\\mu_{t}}_{\\text{trend}} + \\underbrace{c_{t}}_{\\text{cycle}} + \\underbrace{\\varepsilon_t}_{\\text{irregular}}\n",
    "$$\n",
    "\n",
    "The irregular will be assumed to be white noise, and the cycle will be stochastic and damped. The final modeling choice is the specification to use for the trend component. Harvey and Jaeger consider two models:\n",
    "\n",
    "1. Local linear trend (the \"unrestricted\" model)\n",
    "2. Smooth trend (the \"restricted\" model, since we are forcing $\\sigma_\\eta = 0$)\n",
    "\n",
    "Below, we construct `kwargs` dictionaries for each of these model types. Notice that rather that there are two ways to specify the models. One way is to specify components directly, as in the table above. The other way is to use string names which map to various specifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Model specifications\n",
    "\n",
    "# Unrestricted model, using string specification\n",
    "unrestricted_model = {\n",
    "    'level': 'local linear trend', 'cycle': True, 'damped_cycle': True, 'stochastic_cycle': True\n",
    "}\n",
    "\n",
    "# Unrestricted model, setting components directly\n",
    "# This is an equivalent, but less convenient, way to specify a\n",
    "# local linear trend model with a stochastic damped cycle:\n",
    "# unrestricted_model = {\n",
    "#     'irregular': True, 'level': True, 'stochastic_level': True, 'trend': True, 'stochastic_trend': True,\n",
    "#     'cycle': True, 'damped_cycle': True, 'stochastic_cycle': True\n",
    "# }\n",
    "\n",
    "# The restricted model forces a smooth trend\n",
    "restricted_model = {\n",
    "    'level': 'smooth trend', 'cycle': True, 'damped_cycle': True, 'stochastic_cycle': True\n",
    "}\n",
    "\n",
    "# Restricted model, setting components directly\n",
    "# This is an equivalent, but less convenient, way to specify a\n",
    "# smooth trend model with a stochastic damped cycle. Notice\n",
    "# that the difference from the local linear trend model is that\n",
    "# `stochastic_level=False` here.\n",
    "# unrestricted_model = {\n",
    "#     'irregular': True, 'level': True, 'stochastic_level': False, 'trend': True, 'stochastic_trend': True,\n",
    "#     'cycle': True, 'damped_cycle': True, 'stochastic_cycle': True\n",
    "# }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now fit the following models:\n",
    "\n",
    "1. Output, unrestricted model\n",
    "2. Prices, unrestricted model\n",
    "3. Prices, restricted model\n",
    "4. Money, unrestricted model\n",
    "5. Money, restricted model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Output\n",
    "output_mod = sm.tsa.UnobservedComponents(dta['US GNP'], **unrestricted_model)\n",
    "output_res = output_mod.fit(method='powell', disp=False)\n",
    "\n",
    "# Prices\n",
    "prices_mod = sm.tsa.UnobservedComponents(dta['US Prices'], **unrestricted_model)\n",
    "prices_res = prices_mod.fit(method='powell', disp=False)\n",
    "\n",
    "prices_restricted_mod = sm.tsa.UnobservedComponents(dta['US Prices'], **restricted_model)\n",
    "prices_restricted_res = prices_restricted_mod.fit(method='powell', disp=False)\n",
    "\n",
    "# Money\n",
    "money_mod = sm.tsa.UnobservedComponents(dta['US monetary base'], **unrestricted_model)\n",
    "money_res = money_mod.fit(method='powell', disp=False)\n",
    "\n",
    "money_restricted_mod = sm.tsa.UnobservedComponents(dta['US monetary base'], **restricted_model)\n",
    "money_restricted_res = money_restricted_mod.fit(method='powell', disp=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have fit these models, there are a variety of ways to display the information. Looking at the model of US GNP, we can summarize the fit of the model using the `summary` method on the fit object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Unobserved Components Results                            \n",
      "=====================================================================================\n",
      "Dep. Variable:                        US GNP   No. Observations:                  241\n",
      "Model:                    local linear trend   Log Likelihood                 769.632\n",
      "                   + damped stochastic cycle   AIC                          -1527.264\n",
      "Date:                       Tue, 24 Dec 2019   BIC                          -1506.455\n",
      "Time:                               15:05:42   HQIC                         -1518.876\n",
      "Sample:                           01-01-1948                                         \n",
      "                                - 01-01-2008                                         \n",
      "Covariance Type:                         opg                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "sigma2.irregular  1.091e-06    7.3e-06      0.150      0.881   -1.32e-05    1.54e-05\n",
      "sigma2.level      4.078e-06   4.94e-05      0.083      0.934   -9.27e-05       0.000\n",
      "sigma2.trend      2.963e-06    1.4e-06      2.112      0.035    2.14e-07    5.71e-06\n",
      "sigma2.cycle      3.911e-05   2.57e-05      1.524      0.128   -1.12e-05    8.94e-05\n",
      "frequency.cycle      0.4454      0.047      9.474      0.000       0.353       0.537\n",
      "damping.cycle        0.8684      0.042     20.726      0.000       0.786       0.951\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       43.93   Jarque-Bera (JB):                 9.29\n",
      "Prob(Q):                              0.31   Prob(JB):                         0.01\n",
      "Heteroskedasticity (H):               0.27   Skew:                            -0.05\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.96\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "print(output_res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For unobserved components models, and in particular when exploring stylized facts in line with point (2) from the introduction, it is often more instructive to plot the estimated unobserved components (e.g. the level, trend, and cycle) themselves to see if they provide a meaningful description of the data.\n",
    "\n",
    "The `plot_components` method of the fit object can be used to show plots and confidence intervals of each of the estimated states, as well as a plot of the observed data versus the one-step-ahead predictions of the model to assess fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\statespace\\structural.py:1661: RuntimeWarning: invalid value encountered in sqrt\n",
      "  std_errors = np.sqrt(component_bunch['%s_cov' % which])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = output_res.plot_components(legend_loc='lower right', figsize=(15, 9));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, Harvey and Jaeger summarize the models in another way to highlight the relative importances of the trend and cyclical components; below we replicate their Table I. The values we find are broadly consistent with, but different in the particulars from, the values from their table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>$\\sigma_\\zeta^2$</th>\n",
       "      <th>$\\sigma_\\eta^2$</th>\n",
       "      <th>$\\sigma_\\kappa^2$</th>\n",
       "      <th>$\\rho$</th>\n",
       "      <th>$2 \\pi / \\lambda_c$</th>\n",
       "      <th>$\\sigma_\\varepsilon^2$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Series</th>\n",
       "      <th>Time range</th>\n",
       "      <th>Restrictions</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US GNP</th>\n",
       "      <th>1948:1-2008:1</th>\n",
       "      <th>None</th>\n",
       "      <td>40.78</td>\n",
       "      <td>29.63</td>\n",
       "      <td>391.1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>14.11</td>\n",
       "      <td>10.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">US Prices</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1948:1-2008:1</th>\n",
       "      <th>None</th>\n",
       "      <td>26.53</td>\n",
       "      <td>31.8</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.92</td>\n",
       "      <td>9.93</td>\n",
       "      <td>16.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>22.02</td>\n",
       "      <td>-</td>\n",
       "      <td>49.25</td>\n",
       "      <td>0.89</td>\n",
       "      <td>15.35</td>\n",
       "      <td>5.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">US monetary base</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1948:1-2008:1</th>\n",
       "      <th>None</th>\n",
       "      <td>63.45</td>\n",
       "      <td>19</td>\n",
       "      <td>196.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>$\\sigma_\\eta^2 = 0$</th>\n",
       "      <td>18.91</td>\n",
       "      <td>-</td>\n",
       "      <td>247.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    $\\sigma_\\zeta^2$  \\\n",
       "Series           Time range    Restrictions                            \n",
       "US GNP           1948:1-2008:1 None                            40.78   \n",
       "US Prices        1948:1-2008:1 None                            26.53   \n",
       "                               $\\sigma_\\eta^2 = 0$             22.02   \n",
       "US monetary base 1948:1-2008:1 None                            63.45   \n",
       "                               $\\sigma_\\eta^2 = 0$             18.91   \n",
       "\n",
       "                                                    $\\sigma_\\eta^2$  \\\n",
       "Series           Time range    Restrictions                           \n",
       "US GNP           1948:1-2008:1 None                           29.63   \n",
       "US Prices        1948:1-2008:1 None                            31.8   \n",
       "                               $\\sigma_\\eta^2 = 0$                -   \n",
       "US monetary base 1948:1-2008:1 None                              19   \n",
       "                               $\\sigma_\\eta^2 = 0$                -   \n",
       "\n",
       "                                                    $\\sigma_\\kappa^2$  $\\rho$  \\\n",
       "Series           Time range    Restrictions                                     \n",
       "US GNP           1948:1-2008:1 None                             391.1    0.87   \n",
       "US Prices        1948:1-2008:1 None                              0.11    0.92   \n",
       "                               $\\sigma_\\eta^2 = 0$              49.25    0.89   \n",
       "US monetary base 1948:1-2008:1 None                             196.3    0.89   \n",
       "                               $\\sigma_\\eta^2 = 0$              247.3    0.89   \n",
       "\n",
       "                                                    $2 \\pi / \\lambda_c$  \\\n",
       "Series           Time range    Restrictions                               \n",
       "US GNP           1948:1-2008:1 None                               14.11   \n",
       "US Prices        1948:1-2008:1 None                                9.93   \n",
       "                               $\\sigma_\\eta^2 = 0$                15.35   \n",
       "US monetary base 1948:1-2008:1 None                               23.15   \n",
       "                               $\\sigma_\\eta^2 = 0$                 23.8   \n",
       "\n",
       "                                                    $\\sigma_\\varepsilon^2$  \n",
       "Series           Time range    Restrictions                                 \n",
       "US GNP           1948:1-2008:1 None                                  10.91  \n",
       "US Prices        1948:1-2008:1 None                                  16.02  \n",
       "                               $\\sigma_\\eta^2 = 0$                    5.02  \n",
       "US monetary base 1948:1-2008:1 None                                      0  \n",
       "                               $\\sigma_\\eta^2 = 0$                       0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Table I\n",
    "table_i = np.zeros((5,6))\n",
    "\n",
    "start = dta.index[0]\n",
    "end = dta.index[-1]\n",
    "time_range = '%d:%d-%d:%d' % (start.year, start.quarter, end.year, end.quarter)\n",
    "models = [\n",
    "    ('US GNP', time_range, 'None'),\n",
    "    ('US Prices', time_range, 'None'),\n",
    "    ('US Prices', time_range, r'$\\sigma_\\eta^2 = 0$'),\n",
    "    ('US monetary base', time_range, 'None'),\n",
    "    ('US monetary base', time_range, r'$\\sigma_\\eta^2 = 0$'),\n",
    "]\n",
    "index = pd.MultiIndex.from_tuples(models, names=['Series', 'Time range', 'Restrictions'])\n",
    "parameter_symbols = [\n",
    "    r'$\\sigma_\\zeta^2$', r'$\\sigma_\\eta^2$', r'$\\sigma_\\kappa^2$', r'$\\rho$',\n",
    "    r'$2 \\pi / \\lambda_c$', r'$\\sigma_\\varepsilon^2$',\n",
    "]\n",
    "\n",
    "i = 0\n",
    "for res in (output_res, prices_res, prices_restricted_res, money_res, money_restricted_res):\n",
    "    if res.model.stochastic_level:\n",
    "        (sigma_irregular, sigma_level, sigma_trend,\n",
    "         sigma_cycle, frequency_cycle, damping_cycle) = res.params\n",
    "    else:\n",
    "        (sigma_irregular, sigma_level,\n",
    "         sigma_cycle, frequency_cycle, damping_cycle) = res.params\n",
    "        sigma_trend = np.nan\n",
    "    period_cycle = 2 * np.pi / frequency_cycle\n",
    "    \n",
    "    table_i[i, :] = [\n",
    "        sigma_level*1e7, sigma_trend*1e7,\n",
    "        sigma_cycle*1e7, damping_cycle, period_cycle,\n",
    "        sigma_irregular*1e7\n",
    "    ]\n",
    "    i += 1\n",
    "    \n",
    "pd.set_option('float_format', lambda x: '%.4g' % np.round(x, 2) if not np.isnan(x) else '-')\n",
    "table_i = pd.DataFrame(table_i, index=index, columns=parameter_symbols)\n",
    "table_i"
   ]
  }
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